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Approximate Neural Economic Set-Point Optimisation for Control Systems

  • Maciej Ławryńczuk
  • Piotr Tatjewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

This paper describes a neural approach to economic set-point optimisation which cooperates with Model Predictive Control (MPC) algorithms. Because of high computational complexity, nonlinear economic optimisation cannot be repeated frequently on-line. Alternatively, an additional steady-state target optimisation based on a linear or a linearised model and repeated as often as MPC is usually used. Unfortunately, in some cases such an approach results in constraint violation and numerical problems. The approximate neural set-point optimiser replaces the whole nonlinear economic set-point optimisation layer.

Keywords

Process control set-point optimisation Model Predictive Control neural networks optimisation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Maciej Ławryńczuk
    • 1
  • Piotr Tatjewski
    • 1
  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarsawPoland

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